Multi-mode particle filter for bearing remaining life prediction

Peng Wang, Ruqiang Yan, Robert X. Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


As a critical element in rotating machines, remaining useful life (RUL) prediction of rolling bearings plays an essential role in realizing predictive and preventative machine maintenance in modern manufacturing. The physics of defect (e.g. spall) initiation and propagation describes bearing's service life as generally divided into three stages: normal operation, defect initiation, and accelerated performance degradation. The transition among the stages are embedded in the variations of monitored data, e.g., vibration. This paper presents a multi-mode particle filter (MMPF) that is aimed to: 1) automatically detect the transition among the three life stages; and 2) accurately characterize bearing performance degradation by integrating physical models with stochastic modeling method. In MMPF, a set of linear and non-linear modes (also called degradation functions) are first defined according to the physical/empirical knowledge as well as statistical analysis of the measured data (e.g. vibration). These modes are subsequently refined during the particle filtering (PF)-based bearing performance tracking process. Each mode corresponds to an individual performance scenario. A finitestate Markov chain switches among these modes, reflecting the transition between the service life stages. Case studies performed on two run-To-failure experiments indicate that the developed technique is effective in tracking the evolution of bearing performance degradation and predicting the remaining useful life of rolling bearings.

Original languageEnglish
Title of host publicationManufacturing Equipment and Systems
StatePublished - 2018
EventASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018 - College Station, United States
Duration: Jun 18 2018Jun 22 2018

Publication series

NameASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018


ConferenceASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC 2018
Country/TerritoryUnited States
CityCollege Station

Bibliographical note

Publisher Copyright:
Copyright © 2018 ASME.

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering


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